Collaborative Research: Prior-free probabilistic inferential methods for "large-p-small-n" linear regression problems

合作研究：“大-p-小-n”线性回归问题的无先验概率推理方法

基本信息

批准号：
1208841
负责人：
Chuanhai Liu
金额：
$ 8.5万
依托单位：
Purdue University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2012
资助国家：
美国
起止时间：
2012-06-15 至 2015-05-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1208841&HistoricalAwards=false
关键词：
Collaborative Research Prior free probabilistic

项目摘要

The investigators study prior-free probabilistic inference with "large p, small n" regression analysis. This is made possible in the new framework of Inferential Models (IMs) proposed recently by the investigators. Statistical results produced by IMs are probabilistic and have desirable frequency properties. In this study, the investigators develop IM-based methods for linear and certain non-linear regression analysis. A sequence of topics in the context of large-p-small-n regression to be investigated include (1) variable selection in Gaussian regression models; (2) robust Student-t regression; and (3) binary regression models.Linear regression is one of the most commonly used methodologies in statistical applications. However, desirable prior-free and frequency-calibrated probabilistic inference, particularly in the important variable selection context, has not been available until the recent development of IMs. The IM framework provides a new and promising alternative to the well-known Bayesian and frequentist methods for various high-dimensional problems researchers currently face. In this study, the investigators develop new statistical methods and computing software, generating useful tools for applied statisticians and scientists who are challenged by very-high-dimensional data in carrying out regression analysis.

研究人员通过“大 p，小 n”回归分析来研究无先验概率推理。研究人员最近提出的新推理模型（IM）框架使这成为可能。 IM 产生的统计结果是概率性的并且具有理想的频率特性。在本研究中，研究人员开发了基于 IM 的线性和某些非线性回归分析方法。要研究的大p小n回归背景下的一系列主题包括（1）高斯回归模型中的变量选择； (2)稳健的Student-t回归； (3)二元回归模型。线性回归是统计应用中最常用的方法之一。然而，直到最近 IM 的发展，才出现了理想的无先验和频率校准的概率推理，特别是在重要的变量选择环境中。 IM 框架为研究人员当前面临的各种高维问题提供了一种新的、有前途的替代方案，可以替代著名的贝叶斯和频率论方法。在这项研究中，研究人员开发了新的统计方法和计算软件，为在进行回归分析时面临极高维数据挑战的应用统计学家和科学家提供了有用的工具。